Method for measuring distances and speeds of several objects by means of an fmcw radar

ABSTRACT

A method for measuring distance and velocity at a plurality of objects using FMCW radar, in which measurements are repeated cyclically using at least two different frequency ramps, in each measurement, the transmitted signal is mixed with the received signal, and the spectrum of the mixed signal is recorded, in a matching procedure, the peaks that are in the spectra recorded for various ramps and that belong to the same object are allocated to each other, and the distances and velocities of the objects are calculated from the frequencies of the peaks, and in a tracking procedure, the objects measured at various times are identified with one another on the basis of the consistency of their distance and velocity data, wherein each measuring cycle includes not more than three measurements with different frequency ramps, for each plausible combination of two peaks, of which one was recorded during a first measurement and the other was recorded during a second measurement of the same cycle, the distance and the velocity of one possible object represented by these peaks are calculated, the anticipated result of at least one further measurement is calculated from the distance and the velocity of the possible object, and the possible object is discarded if at least one anticipated result does not agree with the measured result.

FIELD OF THE INVENTION

The present invention relates to a method for measuring distance andvelocity at a plurality of objects using FMCW radar.

BACKGROUND INFORMATION

Discussed herein is a method for measuring distance and velocity at aplurality of objects using FMCW radar, in which:

measurements are repeated cyclically with at least two differentfrequency ramps,

in each measurement, the transmitted signal is mixed with the receivedsignal, and the spectrum of the mixed signal is recorded,

in a matching procedure, the peaks that are in the spectra recorded forvarious ramps and that belong to the same object are allocated to eachother, and the distances and velocities of the objects are calculatedfrom the frequencies of the peaks, and

in a tracking procedure, the objects measured at various times areidentified with one another on the basis of the consistency of theirdistance and velocity data.

In particular, discussed herein is a method of this type which is usedin ranging systems or distance-control systems for motor vehicles.

From practice, a distance-control system, a so-called ACC (adaptivecruise control) system for motor vehicles is known which works with anFMCW (frequency modulated continuous wave) radar. The functioningprinciple is described in Winner: “Adaptive Cruise Control”, AutomotiveElectronics Handbook, published by Ronald K. Jurgen, 2^(nd) edition,McGraw-Hill Inc., Chapter 30.1 (1999). The radar waves are emittedcontinuously, and the frequency is modulated in accordance with a rampfunction made up of a cyclical sequence of four ramps having differentslopes. The ramps form two pairs, each made up of a rising and a fallingramp. The amounts of the slopes are identical within each pair, butdiffer from pair to pair. By mixing the transmitted signal with thereceived signal, which is obtained by reflection of the radar waves at aplurality of objects, a low-frequency signal is formed whose frequencycorresponds to the difference between the frequency of the transmittedsignal and the frequency of the reflected signal. In each individualmeasurement, the spectrum of the low-frequency signal is recorded duringthe duration of one frequency ramp with constant slope. In thisspectrum, each object is represented by a peak whose frequency f,according to the following equation, is a function of the distance d andthe velocity v (relative velocity) of the object:f=|(2*F/c*T)*d+(2*f _(s) /c)*v|  (1)

Meanings of equation terms:

-   f peak frequency in the low-frequency signal-   F frequency deviation (frequency at the end of the ramp−frequency at    the beginning of the ramp)-   c speed of light-   T modulation duration (of the ramp)-   f_(s) frequency of the transmitted signal

The first term in equation (1) is proportional to the signal propagationtime, d/c and the ramp slope F/T. The second term corresponds to theDoppler shift of the reflected signal.

If only one reflecting object is present, distance d and relativevelocity v of this object may be calculated from peak frequencies f₁ andf₂, which are obtained by two measurements with different ramp slopes.To that end, the following equation system is solved:f ₁ =α*d+β*vf ₂ =γ*d+β*v  (2)with:α=2*f/c*T for the first ramp,β=2*f _(s) /cγ=2*F/c*T for the second ramp

Given a plurality of objects, however, ambiguities occur, because it isnot clear which peak belongs to which object. In the known method, thisambiguity is eliminated by performing two additional measurements usinga different ramp slope. Since the distances and relative velocities ofthe objects change slightly at most within the time in which the fourmeasurements are performed, the allocation between the peaks and theobjects must be carried out so that the same distances and relativevelocities are obtained for the first two measurements as for the lasttwo measurements. This allocation of the peaks to the objects is calledmatching.

For practical applications, for example, in an ACC system, it isgenerally necessary to be able to track the measured distances andrelative velocities of the various objects over a longer period of time.Therefore, in a procedure known as tracking, the objects measured in onemeasuring cycle must be identified with the objects measured in apreceding cycle. This tracking procedure is based on the criterion thatthe distances and relative velocities for each object, measured atvarious times, must yield a plausible and, in particular, physicallypossible movement of the object.

The U.S. Pat. No. 5,600,561 discusses a method in which only thedistances are measured with the aid of radar, and the object velocitiesare calculated from the changes in distance. The distances measured forvarious objects are allocated to the individual objects in such a waythat the newly recorded distance data are consistent with the previouslycalculated velocities.

In contrast, an FMCW radar has the advantage that the relativevelocities of the objects can be measured directly. However, it is onlypossible to differentiate various objects from each other both withrespect to their distances and with respect to their relative velocitieswith a limited resolution.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a method for measuringdistance and velocity using FMCW radar that permits improved objectresolution.

This objective may be achieved according to the present invention in amethod of the type indicated at the outset, in that

each measuring cycle includes not more than three measurements withdifferent frequency ramps,

for each plausible combination of two peaks, of which one was recordedduring a first measurement and the other was recorded during a secondmeasurement of the same cycle, the distance and the velocity of onepossible object represented by these peaks are calculated,

the anticipated result of at least one further measurement is calculatedfrom the distance and the velocity of the possible object, and

the possible object is discarded if at least one anticipated result doesnot agree with the measured result.

One reason for the limited resolution capability of FMCW radar is thatthe peaks occurring in the individual spectra each have a finite width.In this context, a “blur relation” exists between the width of thesepeaks and the time available for recording the spectra. If, for example,the low-frequency signals are sampled to obtain discrete spectra with anumber n of sampling values, then sampling time T/n (T=modulationduration) is available for each sampling value. In this case, theresolution with respect to the distance is proportional to modulationamplitude F, and the resolution with respect to the relative velocity isproportional to modulation duration T.

Compared to the other method discussed above, the exemplary method ofthe present invention now has the advantage that not four, but rather atmost three measurements are performed within each measuring cycle, sothat given the same cycle duration, a greater modulation duration isavailable, and better resolution with respect to the relative velocityis achieved accordingly.

Ambiguities in the case of a plurality of objects are eliminated in themethod according to the present invention by linking the matchingprocedure and the tracking procedure to each other. If m objects are inthe locating range of the radar, during the first measurement, oneobtains peaks at the frequencies f(1,i), i=1 . . . m, and during thesecond measurement, peak frequencies f(2,j), j=1, . . . , m areobtained. Each pair (i, j) of peaks is regarded as a possible objectwhich can be assigned a distance d_(i),j and a relative velocityv_(i),j. From the distance and relative velocity data thus obtained foreach possible object, it is possible to calculate what result ought tobe anticipated for this object in a further measurement. Depending onthe embodiment variant of the method, this further measurement may be adistance and velocity measurement in a different measuring cycle or athird measurement within the same cycle, using a ramp slope differentfrom the two first measurements. The anticipated result is then comparedto the result actually obtained during the further measurement, and ifthese results do not match, the object in question is discarded.Therefore, only the distance and velocity data remain for those peakpairs which correspond to real objects.

In one specific embodiment, the measuring cycle includes only twomeasurements with equal and opposite ramp slopes. In the case of thegiven cycle duration, the modulation duration is then twice as great asfor the conventional methods which work with four measurements.Accordingly, the resolution with respect to the relative velocities isimproved by a factor of 2. Another advantage is that all measurementsmay be performed with a maximum frequency deviation, in which theavailable frequency range of the microwave transmitter is fullyutilized. Correspondingly, a maximum resolution with respect to thedistance is also achieved in each measurement, while when working withthe conventional method, two of the four measurements had to beperformed with a smaller ramp slope, and accordingly, with a smallerfrequency deviation. Overall, therefore, given a relatively small cycleduration—and correspondingly high time resolution in the object searchand object tracking—a high resolution may be achieved both with respectto the distance measurement and with respect to the relative-velocitymeasurement.

In this specific embodiment, the further measurement, whose result iscompared to the anticipated result, is a distance and relative-velocitymeasurement in an earlier or later measuring cycle. The agreement of theresults then means not only that the possible object in question is areal object, but at the same time means that the object was alsoidentified with an object in the earlier or later measuring cycle, sothat the tracking procedure was successful.

For example, from the distance measured in the current cycle and theappertaining relative velocity, it is calculated what distance the sameobject would have to have had in the immediately preceding measuringcycle. In so doing, it may be assumed in the simplest case that thechange in relative velocity is negligibly small from measuring cycle tomeasuring cycle.

Alternatively, however, an expanded tracking procedure may also be used,in which not only the immediately preceding measuring cycle, but rathera larger number of previous measuring cycles is taken into account. Itis possible that, because of interference effects, no echo was receivedfrom a real object within a single measuring cycle. In this case, theexpanded tracking procedure with consideration of a plurality ofmeasuring cycles offers the advantage that the object can neverthelessbe recognized as a real object and successfully tracked.

Instead of calculating the anticipated distance and the anticipatedrelative velocity of the object for a previous measuring cycle andcomparing them to the actually measured values, in a modified specificembodiment, it is also possible, from the data measured in theinstantaneous cycle, to directly calculate the frequency at which thepeak for this object ought to be found in the other (earlier or later)cycle. The result can then be very easily verified by specificallysearching for a peak at this location. An expanded tracking withconsideration of a plurality of measuring cycles is possible in thisvariant, as well.

In the specific embodiment of the method which works with threemeasurements within one measuring cycle, the third measurement which mayhave a greater modulation duration than the two first measurements. Inthis way, a particularly high resolution with respect to the relativevelocity is achieved especially in the third measurement. It is againpossible to also work with maximum frequency deviation in the thirdmeasurement, so that here as well, a maximum distance resolution isachieved in all measurements.

This specific embodiment may also be combined with the tracking, takinginto account the immediately preceding measuring cycle or a plurality ofpreceding measuring cycles. For example, it can be required for realobjects that the corresponding frequency was measured in two successivemeasurements in all three spectra. Alternatively, however, this methodmay also be combined with the tracking in such a way that theconfirmation as to whether a real object is involved is carried outdepending on the situation with the aid of the simple tracking, with theaid of the third frequency ramp, or with both. In this context, thethird ramp and the tracking may also be linked by “or”, so that anobject is recognized as real if only one of the anticipated results isconfirmed, be it the result for the previous measuring cycle or theresult for the measurement with the third frequency ramp. An expandedtracking with consideration of more than two measuring cycles ispossible in all combinations when working with these variants, as well.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a frequency/time diagram for clarifying the functioningmethod of an FMCW radar.

FIG. 2 shows examples for spectra that are recorded using the FMCWradar.

FIG. 3 shows a diagram for clarifying a method for determining theobject distance.

FIG. 4 shows a diagram analogous to FIG. 3, but for a larger frequencydeviation of the radar signal.

FIG. 5 shows a diagram for clarifying a method for determining therelative velocities of objects.

FIG. 6 shows a diagram analogous to FIG. 5, but for a larger frequencydeviation of the radar signal.

FIG. 7 shows a flow chart for clarifying the general principle of afirst specific embodiment of the method according to the presentinvention.

FIG. 8 shows a detailed flow chart of the method according to FIG. 7.

FIG. 9 shows a flow chart of a subroutine in the method according toFIG. 8.

FIG. 10 shows an exemplary embodiment for a further subroutine in themethod according to FIG. 8.

FIG. 11 shows another exemplary embodiment for a further subroutine inthe method according to FIG. 8.

FIG. 12 shows a flow chart for a modification of the method according toFIG. 8.

FIG. 13 shows an example for a subroutine in the method according toFIG. 12.

FIG. 14 shows another example for a subroutine in the method accordingto FIG. 12.

FIG. 15 shops a frequency/time diagram for another specific embodimentof the method.

FIG. 16 shows a flowchart for a variant of the method according to FIG.15.

FIG. 17 shows a flowchart for another variant of the method according toFIG. 15.

FIG. 18 shows a flowchart for another variant of the method according toFIG. 15.

DETAILED DESCRIPTION

According to FIG. 1, when working with an FMCW radar, radar waves arecontinually emitted with variable frequency f_(s). Curve 10 drawn in inbold indicates the time dependency of frequency f_(s). This frequency isvaried periodically according to a ramp function having a rising firstramp 12 and a falling second ramp 14. Ramps 12, 14 belong to twomeasurements M1 and M2, which are repeated cyclically. The ramps forboth measurements have the same frequency deviation F and identicalmodulation durations T1 and T2, and consequently their slopes are equaland opposite.

Frequency curve 16 for the associated radar echo of a single object isshown with a broken line. This curve has the same ramps 18 and 20, butwith a time shift Δt, which is given by the object distance, and with afrequency shift Δf determined by the Doppler shift.

In the radar sensor, the emitted wave is mixed with the received radarecho, so that a low-frequency beat signal (LF signal) is obtained havinga frequency f which corresponds to the frequency difference between theemitted waves and the received waves. During each measurement M1, M2, afrequency spectrum is recorded from this beat signal, e.g. by rapidFourier transform.

FIG. 2 shows examples for two frequency spectra 22, 24 obtained in thetwo measurements M1 and M2 when a single object, e.g. a precedingvehicle, is located in the radar locating range. In this case, eachspectrum has a peak with an apex at the peak frequency f₁ and f₂,respectively, which corresponds to the frequency difference of curves 10and 16 on respective ramps 12 and 18 or 14 and 20. The frequencydifference is substantially dependent on the product of time shift Δt(signal propagation time) and the ramp slope; however during risingflanks 12, 18, it is reduced by the Doppler frequency, whereas duringfalling flanks 14, 20, it is increased by the Doppler frequency (givenpositive Doppler shift to greater frequencies, corresponding to anapproach of the radar target). The average value of frequencies f₁ andf₂, at which the two peaks 22, 24 lie in FIG. 2, therefore correspondsto the signal propagation time, and thus indicates distance d of theobject, while half the difference between these two frequenciesindicates the Doppler shift, and therefore the amount and algebraic signof relative velocity v of the object. Relative velocity v isproportional to (f₁−f₂)/2.

During the duration of a single measurement M1 or M2, the frequency ofthe LF-signal whose spectrum is shown in FIG. 2 is largely constant.Nevertheless, even given sharply defined object distances d and relativevelocities v, peaks 22, 24 have a certain width which, because of theproperties of the Fourier transform, is approximately inverselyproportional to the measuring time. On its part, the measuring timeavailable is proportional to modulation duration T1 and T2,respectively.

When, in the radar locating range, there are two objects whose peaks areso close to each other that their distance is small in relation to thewidth of the peaks, then the corresponding peaks in the spectrum can nolonger be resolved, and consequently both objects can no longer bedifferentiated from each other. The more sharply the peaks are defined,i.e., the greater the modulation durations T1, T2, the better theresolution is.

The resolution with respect to the object distance may be improved byenlarging frequency deviation F. This shall be clarified with referenceto FIGS. 3 and 4.

In FIG. 3, in addition to curve 10, curves 26, 28 of two radar echoes,which are received from two different objects, are drawn in. For eachcurve 26, 28, differences D26 and D28, respectively, between the peakfrequencies are indicated on the first and second ramp. This differenceis independent of the Doppler shift, and therefore solely represents thepropagation time, and thus object distance d.

FIG. 4 shows the same for a larger frequency deviation F. One can seethat here, differences D26 and D28 are enlarged proportionally to thefrequency deviation, so that the peaks can be differentiated moreclearly. The same spread of differences D26 and D28 could also beachieved by reducing the modulation durations and leaving frequencydeviation F unchanged, so that the same ramp slopes are attained as inFIG. 4. However, the larger spread would then not lead to a higherresolution, since because of the shorter measuring duration, the peakswould widen accordingly. The decisive parameter for the resolution ofthe distances is therefore frequency deviation F. In the exampledescribed here, in both measurements M1 and M2, use is made of themaximum frequency deviation achievable based on the design of the radardevice.

FIGS. 5 and 6 illustrate that no better resolution with respect to therelative velocities can be achieved by increasing frequency deviation F.In FIG. 5, sums S26 and S28 of the frequency differences are indicatedon the two ramps for the two curves 26 and 28. The distance-dependentpropagation-time differences have the exactly opposite effect on thefrequency difference in the two ramps, and therefore cancel each otherout in the summation, so that sums S26 and S28 solely represent theDoppler shift for the objects in question. Since this Doppler shift isnot a function of the frequency deviation, sums S26 and S28 in FIG. 6are not greater than in FIG. 5. Therefore, improved resolution withrespect to the relative velocities may only be achieved by longermodulation durations T1, T2.

On the other hand, however, the total cycle time T=T1+T2 should not betoo great, so that the objects detected in one measuring cycle are ableto be detected again in the next measuring cycle with the aid of atracking procedure based on similar distances and relative velocities,and so that the movements of the objects may be tracked with high timeresolution. In the specific embodiment shown here, it is possible tosatisfy these contradictory demands, since only two measurements need tobe carried out during one measuring cycle, so that the modulationduration is relatively great (half as great as the cycle duration).However, a matching procedure is needed which, based on the twomeasurements per measuring cycle, permits the elimination of ambiguitiesin the detection of a plurality of objects, by correctly allocating thepeaks recorded during the first measurement and the peaks recordedduring the second measurement to each other.

This is achieved in the method described here by combining the matchingprocedure with the tracking procedure, as presented in broad outline inFIG. 7.

To that end, in step S1 in FIG. 7, first of all the distances andrelative velocities are calculated for all “possible objects” [i, j]. Inthis context, all pairs [i, j] of peaks are regarded as “possibleobjects”, i being the current number of all peaks from the spectrumrecorded in the first measurement M1, and j being the current number ofall peaks from the spectrum recorded in measurement M2. If a total of mobjects are present, there are m peaks in each spectrum, and the numberof possible objects is m². The real objects are represented by a subset,made up of m pairs, of the quantity of all possible objects.

In step S2, the real objects are differentiated from the unreal objectsby utilizing the history, i.e., the results of preceding measurements.In so doing, the criterion is that for real objects, a plausibleconnection must exist between the results of the current measurement andthe results of one of the previous measurements. For example, it ispossible to compare the distances and relative velocities from theinstantaneous measuring cycle to the distances and relative velocitiesfrom the preceding measuring cycle, as is also usually done in atracking procedure. For real objects, the relative velocities shouldthen be nearly identical, and the difference between the distances mustagree approximately with the product of the relative velocity and cycleduration T. Peak pairs for which no partner satisfying these criteria isfound in at least one of the preceding measurements are sorted out as“false solution”, i.e., as unreal objects. In so doing, however, realobjects which were detected by the radar device for the first time inthe current measuring cycle are also mistakenly sorted out. So thatthese objects can be recognized in later measuring cycles, in step S3,the results for all possible objects are stored. For the furtherevaluation, however, e.g. for the distance control in a motor vehicle,in step S4 only the “plausible” peak pairs recognized as real objectsare utilized.

FIG. 8 shows the method sequence in greater detail.

Following step S1, in step S21 the first possible object is selectedfrom the list of all possible objects. In step S22, it is then estimatedwhat distance and what relative velocity this object would have to havehad in the preceding measuring cycle. Based on the results which werestored in the preceding measuring cycle in step S3, it is then checkedin step S23 whether an object with appropriate distance and appropriaterelative velocity is actually found among the objects from the precedingcycle. If this is the case, then in step S24, the object which wasselected in step S21 is entered into the list of real objects.Otherwise, the object is discarded.

In step S25, it is checked whether all objects of the instantaneouscycle have already been verified. If this is not the case, in step S26,the next object is selected from the current list and there is a returnto step S22. All objects are then verified in this manner one afteranother in a loop using steps S22-S26. After the last object is checked,the loop is left after step S25.

It is possible that the list of possible objects which was stored in thepreceding cycle will still contain objects not found again in thecurrent cycle. These objects are now deleted in step S27. Therefore, inthis specific embodiment, only simple tracking is carried out in whichthe current objects are compared only to the objects from theimmediately preceding measuring cycle.

Step S1 in FIGS. 7 and 8 is made up of a subroutine whose flow chart isshown in FIG. 9. In this context, it is assumed that the spectrarecorded in the instantaneous cycle during the first measurement andduring the second measurement each have a number of peaks correspondingto the number of objects and in each case lying at a certain frequency.Therefore, given m objects, each spectrum contains m frequencies atwhich a peak is located. In step S11, the first of these frequencies isselected from the first spectrum. Correspondingly, in step S12, thefirst frequency is selected from the second spectrum. In step S13, afirst plausibility check is carried out based on the criterion that theamplitudes in the case of the first frequency in the first spectrum andthe first frequency in the second spectrum must have similar values, ifthe peak pair in question is a real object. If the amplitudes clearlydiffer, they are obviously not echoes from the same object, so that thepeak pair in question may be discarded from the start.

In step S14, a second plausibility check is carried out on the basis ofthe criterion that the signals which were reflected by the same objectmust also come from a similar direction. If this criterion is not metfor the peak pair being considered, this peak pair may likewise bediscarded. The checks in steps S13 and S14 make it possible to reducethe computing expenditure, however are not essential for the method.

In step S15, the actual calculation of distance d and relative velocityv is then carried out for the possible object represented by the peakpair being examined. This calculation is performed by solving the systemof equations (2) indicated in the introductory section of theSpecification. Here, frequency f (1,i) is the frequency for peak i inthe first spectrum, and frequency f (2,j) is the frequency of peak j inthe second spectrum. If the plausibility check in step S13 or S14 wasnegative, step S15 is skipped. Therefore, the calculations in step S15are not carried out for each combination of peaks, but rather only forthose combinations of peaks which are plausible in the sense that theysatisfy the criteria checked in steps S13 and S14. Only these peak pairsremain stored in the list of possible objects.

Steps S16 through S19 control the program run in two loops nested in oneanother, in which all combinations of peaks i and j in the two spectraare checked in succession.

After distances d and relative velocities v have been calculated in thisway for all possible, at least for all plausible, objects, in light ofthis data, step S22 in FIG. 8 may be carried out in which, based on therelative velocities, there is a calculation back to find what distancesthe objects in question had in the preceding measuring cycle.Conversely, it is naturally also possible to proceed so that, based onthe distance and velocity data obtained in the instantaneous cycle, thedistances and relative velocities to be anticipated for these objects inthe next measuring cycle are precalculated. FIG. 10 shows a possibleimplementation of step S23 in FIG. 8 based on the latter principle.

In step S231, the values predicted in the preceding measuring cycle forthe distance and the relative velocity and optionally also for thelateral displacement of the first possible object are read. In stepS232, these predicted values are then compared to the values which wereobtained in the instantaneous cycle for the currently observed object(selected in step S21 in FIG. 8). If the deviations are withinpermissible tolerance limits, in step S233, the two objects are linkedto each other (tracking). Otherwise, step S233 is skipped.

Steps S234 and S235 control a loop with which the above-described checksare repeated in succession for each possible object from the previousmeasuring cycle. If it was possible to link the instantaneous object instep S233 to one of the objects from the previous measuring cycle, thenthe query in S23 is answered with “yes”, and the method is continuedwith step S24. Otherwise, the query must be answered with “no”, andthere is a jump to step S25.

FIG. 11 shows another possible implementation of step S23 in FIG. 8.From the distance of the object, estimated in step S22, at the moment ofthe preceding measuring cycle, and from the relative velocity of thisobject (assumed as approximately constant), in step S231′, thefrequencies are calculated at which the peaks in the two spectrarecorded in the preceding cycle would have had to be situated. Thefrequency calculation is performed according to equation (1) indicatedin the introductory part of the Specification. In step S232′, it is thenchecked whether these frequencies have actually been measured. If thisis the case, step S23 ends with the response “yes” (step S233′),otherwise with the response “no” (step S234′).

Naturally, the variant according to FIG. 11 may also be carried out“forwards” by calculating the frequencies to be anticipated for thefollowing measuring cycle.

FIG. 12 shows a variant with respect to the program sequence accordingto FIG. 8. This variant differs from FIG. 8 essentially in that step S27in FIG. 8 is replaced by a step S28 in which a so-called expandedtracking is carried out. In this context, the check test in step S23 maybe performed both according to the method in accordance with FIG. 10(object matching) and according to the method in accordance with FIG. 11(frequency matching). FIG. 13 shows one possible implementation of stepS28 for the first case.

In FIG. 13, the first element is selected from the list of real objectsin step S281. In step S282, it is checked whether this object was foundagain in the current measuring cycle (positive result in response to thequery in step S23). If this is not the case, there is a certainprobability that this object has disappeared from the locating range ofthe radar, i.e., it is less plausible that this object will appear againin later measurements. Accordingly, in step S283, a plausibilityparameter for this object is decreased. In step S284, it is then checkedwhether the plausibility is still above a specific threshold value. Ifthis is not the case, the object is discarded in step S285, i.e., it ispermanently removed from the list of real objects.

In the event of a positive result in step S284, the object continues tobe handled as a real object, but no current measuring data exists forthe distance and the relative velocity. Therefore, this data isestimated in step S286 by extrapolating the previous measuring data.

If the object was found again in step S282, in step S287, it is checkedwhether the plausibility parameter for this object has already reachedan upper limiting value. If this is not the case, the plausibility isincreased in step S288. Otherwise, step S288 is skipped.

Steps S289 and S290 again control a loop, in which the above check testsare repeated for each object in the list of real objects.

FIG. 14 shows an implementation of step S28 for the case when frequencymatching according to FIG. 11 is used in step S23. In comparison to FIG.13, the subroutine according to FIG. 14 has an additional step S291 inwhich, from the estimated data for the position and relative velocity ofthe object not found again, the associated frequencies in the first andsecond spectrum are calculated. These frequencies are then regarded as“measured” in the next measuring cycle in the case of step S282′.

The expanded tracking in step S28 makes the method more robust withrespect to a temporary loss of an object. The more frequently the objectis found again, the higher its plausibility becomes—up to an upperlimiting value. Temporary loss of the object leads to a reduction inplausibility, and the object remains in the list of real objects untilthe plausibility has decreased to below a lower limiting value.

With respect to the matching in step S23 and in FIG. 10 or 11, however,a slight modification is necessary in the methods according to FIGS. 12through 14. During the check test in step S23, it is necessary to takeinto account not only the immediately preceding measurement, but ratherall objects still contained in the list of real objects must be includedhere as well, even if they were temporarily lost during the immediatelypreceding measurement. As an alternative, in step S282 (FIG. 13), thecheck test may be extended to those objects of the current cycle whichwere initially discarded in step S23. For these objects, step S24(inclusion in the list of real objects) can then optionally beretrieved.

Another specific embodiment of the method shall now be described withreference to FIGS. 15 through 18.

FIG. 15 shows a modified form of frequency curve 10 in FIG. 1. In thisfrequency curve, a rising ramp 30 again adjoins falling ramp 14 beforethe measuring cycle is repeated. Accordingly, three measurements M1, M2and M3 are carried out here within one measuring cycle. In the thirdmeasurement M3, modulation duration T3 is twice as great as in the twoother measurements. The slope of ramp 30 is only half as great as forramp 12. Consequently, frequency deviation F is also at its maximum inthird measurement M3. The object distances are therefore able to bemeasured with optimal resolution in the third measurement, as well. Withrespect to the relative velocities, because of the greater modulationduration, a resolution is achieved in the third measurement which istwice as high as in measurements M1 and M2. The cycle duration isprecisely as great here as in the conventional method, in which fourmeasurements are performed within one cycle. The advantage of the methodaccording to FIG. 15 compared to the conventional method is that theobject distances may be measured three times with maximum resolutionwithin one cycle, and the relative velocities even one time with doublethe resolution.

Instead of the results of measurements M1 and M2 in the preceding cyclesor in addition thereto, the result of measurement M3 in the currentcycle or in a preceding or subsequent cycle is utilized here for thematching.

An example for the method sequence is shown in FIG. 16. Steps S101 andS102 in FIG. 16 correspond to steps S1 and S21 in FIG. 8. In step S103,for each possible object whose distances and relative velocities werecalculated in step S101 on the basis of measurements M1 and M2 in thecurrent cycle, the anticipated frequency of the respective peak in thespectrum, which is recorded in measurement M3 for ramp 30, is calculatedin accordance with equation (1). In step S104, it is then checkedwhether a peak is actually found at this frequency in the thirdmeasurement. If this is the case, in step S105, the possible object isentered into the list of real objects. Otherwise, step S105 is skipped.Steps S106 and S107 are again used for loop control.

FIG. 17 shows a variant in which, in comparison to FIG. 16, in responseto a negative result of the query in step S104, two steps S108 and S109are carried out. Here, the matching with the aid of third measurement M3is supplemented by simple tracking and object matching analogous to FIG.8. To that end, based on the measured distance and velocity data for theconsidered object, it is estimated in step S108 what distance and whatrelative velocity this object had in the preceding measuring cycle. Instep S109, it is checked whether an object with this distance and thisrelative velocity was present in the preceding cycle. If this was thecase, step S105 is carried out, although the calculated frequency couldnot be confirmed in the third measurement. Only if the query in stepS109 is also negative, is step S105 skipped.

FIG. 18 shows a modification with respect to FIG. 17 in whichadditionally, at the end of the procedure, an expanded trackinganalogous to FIGS. 12 through 14 is carried out in step S110.

1-9. (canceled)
 10. A method for measuring distance and velocity at aplurality of objects using FMCW radar, the method comprising: cyclicallyrepeating, in measuring cycles, measurements of the objects using atleast two different frequency ramps, wherein in each of themeasurements, a transmitted signal is mixed with a received signal toprovide a mixed signal, and a spectrum of the mixed signal is recorded;performing a matching procedure, in which peaks that are in the spectraare recorded for various ramps and that belong to the same object areallocated to each other, and the distances and velocities of the objectsare calculated from frequencies of the peaks; and performing a trackingprocedure, in which the objects measured at various times are identifiedwith one another based on consistency of their distance and velocitydata; wherein: each of the measuring cycles includes not more than threemeasurements with different frequency ramps, for each plausiblecombination of two peaks, of which one was recorded during a firstmeasurement and another of which was recorded during a secondmeasurement of the same cycle, a distance and a velocity of one possibleobject represented by these peaks are calculated, an anticipated resultof at least one further measurement is calculated from the distance andthe velocity of the possible object, and the possible object isdiscarded if at least one anticipated result does not agree with ameasured result.
 11. The method of claim 10, wherein only twomeasurements are performed in each measuring cycle, and a furthermeasurement is a measurement in another measuring cycle.
 12. The methodof claim 11, wherein the anticipated result of the further measurementis at least one of the distance and the relative velocity of the objectin the other measuring cycle.
 13. The method of claim 11, wherein theanticipated result of the further measurement is the frequency of a peakin at least one spectrum, which was recorded in the other measuringcycle.
 14. The method of claim 10, wherein three measurements areperformed in each measuring cycle, and the further measurement is athird measurement, in which a modulation duration of the frequency rampis greater than for the first and second measurements.
 15. The method ofclaim 14, wherein the anticipated result of the further measurement isthe frequency of a peak in the spectrum, which is recorded in thismeasurement.
 16. The method of claim 14, wherein an anticipated resultis also calculated for the first and second measurement in anothermeasuring cycle and compared to the actual result.
 17. The method ofclaim 10, wherein a comparison with results of the further measurementsis performed for a plurality of successive measuring cycles.
 18. Themethod of claim 17, wherein each object is assigned a plausibilityparameter which is increased when the anticipated result agrees with ameasured result from another measuring cycle, and which is reduced whenthe anticipated result does not agree with any of the measured results,and the object is only discarded when the plausibility parameter dropsbelow a predefined threshold value.